Post on 03-Feb-2023
Psicologia, Saúde e Doenças
ISSN: 1645-0086
spps@clix.pt
Sociedade Portuguesa de Psicologia da
Saúde
Portugal
Ribeiro Viseu, João Nuno; Neves de Jesus, Saul; Leal, Rita; Valle, Patrícia; Santos,
Joana; Dias Matavelli, Rafaela; Greenglass, Esther
THE ECONOMIC RECESSION AND MENTAL HEALTH: ANALYSIS OF THE
PORTUGUESE SITUATION
Psicologia, Saúde e Doenças, vol. 16, núm. 2, 2015, pp. 175-188
Sociedade Portuguesa de Psicologia da Saúde
Lisboa, Portugal
Available in: http://www.redalyc.org/articulo.oa?id=36242128004
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THE ECONOMIC RECESSION AND MENTAL HEALTH: ANALYSIS OF THE
PORTUGUESE SITUATION
João Nuno Ribeiro Viseu 1, Saul Neves de Jesus
1, Rita Leal
1 , Patrícia Valle
1 , Joana Santos
1
Rafaela Dias Matavelli1, & Esther Greenglass
2
1Research Centre for Spatial and Organizational Dynamics, University of Algarve, Faro, Portugal;
2York
University, Department of Psychology, Toronto, Canada.
_________________________________________________________________________________
ABSTRACT – Recently, some European countries (e.g., Greece, Ireland, and Portugal)
suffered an economic recession, mainly due to problems in the banking system and high
sovereign debt. Economic stressors, such as economic hardship, financial threat, and financial
well-being may contribute negatively to individuals’ psychological health, potentiating the
appearance of negative psychological outcomes. The objective of the current research was to
analyze the impact of economic stressors (i.e., economic hardship, financial threat, and financial
well-being) on mental health indicators (i.e., stress, anxiety, and depression). Data from a
sample of 729 Portuguese participants was collected, 482 (66.1%) women and 247 (33.9%)
men, with an average age of, approximately, 37 years old (M=36.99; SD=12.81). Structural
Equation Modeling (SEM) was used to examine the relationship between the economic stress
and psychological health variables. The obtained results demonstrated a model with economic
hardship and financial threat as significant predictors of stress, anxiety, and depression, while
financial well-being was a significant predictor of anxiety and depression. Some implications of
these results are discussed.
Key-words: Anxiety, depression, economic stressors, mental health, stress
________________________________________________________________
A RECESSÃO ECONÓMICA E A SAÚDE MENTAL: ANÁLISE DA
SITUAÇÃO PORTUGUESA
RESUMO – Recentemente, alguns países europeus (e.g., Grécia, Irlanda e Portugal)
sofreram uma recessão económica, principalmente devido a problemas no sistema
bancário e a uma elevada dívida soberana. Stressores económicos, tais como
dificuldades económicas, ameaça financeira e bem-estar financeiro podem contribuir
negativamente para a saúde psicológica dos indivíduos, potenciando o aparecimento de
psicopatologia. O objetivo da presente investigação foi analisar o impacto de stressores
económicos (i.e., dificuldades económicas, ameaça financeira e bem-estar financeiro)
em indicadores de saúde mental (stresse, ansiedade e depressão). Foram recolhidos
dados de uma amostra de 729 participantes portugueses, 482 (66,1%) do género
feminino e 247 (33,9%) do masculino, com uma média de idades de, aproximadamente,
37 anos (M=36,99; DP=12,81). Foi utilizado um Modelo de Equações Estruturais para
examinar a relação entre as variáveis de stresse económico e saúde psicológica. Os
Centre for Spatial and Organizational Dynamics, University of Algarve, Campus de
Gambelas – Building 9., 8005-139 Faro, Portugal. Telef.: (+351)961482781. E-mail:
joaonrviseu@gmail.com
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resultados obtidos apresentaram um modelo em que as dificuldades económicas e a
ameaça financeira eram preditores significativos de stresse, ansiedade e depressão, ao
passo que o bem-estar financeiro se assumiu como uma variável preditora da ansiedade
e depressão. Algumas implicações destes resultados são discutidas.
Palavras-chave: Ansiedade, depressão, saúde mental, stresse, stressores económicos.
________________________________________________________________
Recebido em 04 de Novembro de 2014/ Aceite em 06 de Junho de 2015
The current global economic downturn, which emerged in 2007, has had a negative impact on
economies all over the world (Yurtsever, 2011). Various countries (e.g., Greece, Ireland, and
Portugal), have been facing problems in the banking system and vulnerabilities in the sovereign
debt (Monastiriotis, 2013). In order to deal with these situations, some countries (e.g., Portugal)
have implemented austerity measures (Monastiriotis, 2013).
Economic downturns generate uncertainty in populations (Cooper, 2012), especially because of
decreases in: (a) job security; (b) wages; (c) social service benefits; and (d) healthcare expenditures
(Keegan, Thomas, Normand, & Portela, 2013; Marjanovic, Greenglass, Fiksenbaum, & Bell, 2013).
On the other hand, increases in: (a) debt levels; (b) investment losses; (c) unemployment, mainly
youth unemployment; and (d) household costs, also contribute to an increase in threat perceptions
(Boone, van Ours, Wuellrich, & Zweimuller, 2011; Keegan et al., 2013; Mielcova, 2012).
Personal financial situations have worsened, for some to the point of exasperation (Barr, Taylor-
Robinson, Scott-Samuel, McKee, & Stuckler, 2012). According to a study of Sperling, Bleich, and
Reulbach (2008), the public’s initial reaction to the crisis was characterized by fear, anxiety, and a
generalized sense of panic. Several years into the crisis, the mood of populations about their health
and the trajectory of their economies remains largely negative (Barr et al., 2012). Health-related
consequences, such as substance abuse (e.g., alcohol), coronary heart problems, and suicide are
common in difficult economic periods (Catalano, 1991; Ferrie, Kivimaki, Shipley, Smith, &
Virtanen, 2013; Vijayasiri, Richman, & Rospenda, 2012).
Recent studies across the social sciences have shown that the deterioration of personal finances,
which is itself exacerbated by economic downturns, is a major source psychological turmoil
(Althouse, Allem, Childers, Dredze, & Ayers, 2014; Astell-Bur
& Feng, 2013; Catalano et al., 2011; Sargent-Cox, Butterworth, & Anstey, 2011). In fact,
studies have linked personal financial problems as a contributing factor to a wide array of negative
psychosocial outcomes, such as psychological distress, mental illness (Fitch, Hamilton, Bassett, &
Davey, 2011), depression (Mirowsky & Ross,2001), dissatisfaction with life, and dysfunctional
impulsivity (Bechtel, 2012; Norvilitis, Szablicki, & Wilson, 2003).
A set of studies that indicate the existence of an association between the aforesaid constructs
(i.e., distress, anxiety, and depression) and economic hardship, financial threat, and financial well-
being (i.e., economic stressors) is presented below.
In Catalano’s (1991) review about the effects of economic insecurity in health, it was
demonstrated that stress levels increase during adverse economic periods. Aspects such as
unemployment, an aspect that rises in economic recessions, is also associated with distress
(Catalano et al., 2011). Two researches (Butterworth, Rodgers, & Windsor, 2009; Sargent-Cox et
al., 2011) observed that anxiety and depression were significantly correlated with economic
hardship. Similar results were verified by Greenglass, Marjanovic, and Fiksenbaum (2013). A
recent study (Leal, Viseu, Jesus, Paixão, & Greenglass, 2014), conducted in Portugal, presented
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some explanatory results about the influence of financial threat and economic hardship on the
variance of stress, depression, and anxiety.
If financial satisfaction is an integral component of overall life satisfaction and well-being
(Stevenson & Wolfers, 2008), than the accumulation of stressful financial events may cause
financial stress and lower financial well-being. Norvilitis et al. (2003), Diener, Ng, Harter, and
Arora (2010), and Stevenson and Wolfers (2008) demonstrated that perceived financial well-being
is related to one’s overall psychological well-being. Research has also shown that lack of financial
well-being may cause social, physical, and emotional stress (Weisman, 2002).
In the Portuguese case, it is very important to study the psychological impact of the economic
crisis. This country was the third, within Europe, to have an assistance program developed by the
International Monetary Fund (IMF), European Comission (EC), and European Central Bank (ECB)
(Yurtsever, 2011). However, before the execution of the abovementioned program, Portugal was
already implementing austerity measures defined by the country’s Government (Cabral, 2013).
Cabral (2013) argued that the main consequences of the Portuguese crisis were: (a) high sovereign
debt; (b) high external debt; and (c) lack of equilibrium in the balance of payments, since the
volume of imports was higher than the level of exports. Wage cuts, reduction of retirement
pensions, and a large increase in taxes, namely in the value-added tax (VAT), were the key financial
consolidation measures introduced (Cabral, 2013). As a result, and due to the external environment
of the country, it was verified, between 2007-2013, a growth in the number of companies that
requested insolvency or bankruptcy (Directorate-General for Justice Policy, 2013) and a raise in
unemployment taxes, between January-April of 2011 Portugal had 12.4% unemployment rate,
nevertheless that value expanded to 15.8% between January-April 2012 (Cabral, 2013). Relating to
youth unemployment, it was possible to observe an enlargement of this rate by about 20% between
late 2010 and January-March of 2013 (2010: 23%; 2012: 40%; January-March 2013: 42.1%)
(Statistics Portugal, 2013). As a consequence of this situation, there has been an increased flow of
emigration in highly qualified graduates (Cairns, 2013).
The objectives of the current research were to analyze the relationship between economic
stressors (i.e., economic hardship, financial threat, and financial well-being) and mental health
indicators (i.e., stress, anxiety, and depression) (Figure 1). This paper aims to contribute to the
current state of the art about the effects of the current economic recession in mental health,
presenting the case of a country, Portugal, which was severely affected by this adverse economic
environment.
To accomplish this objective, nine research hypotheses were proposed:
Figure 1.
Presentation of the conceptual model with the defined research hypotheses.
H7
Anxiety
Financial
Threat
Economic
Hardship
Financial
Wellbeing
Depression
Stress H1
H2
H3
H4
H5
H6
H8
H9
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To sum up, the following hypotheses were defined:
H1. Financial threat positively influences stress.
H2. Financial threat positively influences anxiety.
H3. Financial threat positively influences depression.
H4. Economic hardship positively influences stress.
H5. Economic hardship positively influences anxiety.
H6. Economic hardship positively influences depression.
H7. Financial well-being positively influences stress.
H8. Financial well-being positively influences anxiety.
H9. Financial well-being positively influences depression.
METHOD
Participants
The study’s sample was composed by 729 Portuguese participants who answered a research
protocol designed by Greenglass (2008). In the present research economic stressors (i.e., economic
hardship, financial threat, and financial well-being) and mental health indicators (i.e., stress,
anxiety, and depression) were evaluated. The respondent’s mean age was, approximately, 37 years
old (M=36.77; SD=12.81). A description of the sample is presented in table 1.
Table 1.
Sample characterization (N=729)
Sample characteristics n %
Gender
Male 247 33.9
Female 482 66.1
Educational background
Elementary school 35 5
High school 211 29
Bachelor’s degree 96 13
Master’s and PhD degrees 379 53
Note. n: Number of individuals per characteristic; %: Percentage of individuals per
characteristic.
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Material
Economic hardship was measured by the Economic Hardship Questionnaire (EHQ) (Lempers,
Clark-Lempers, & Simons, 1989), which presented 10 items with four answer options (1 – Never; 4
– Very often). In its’ validation study (Lempers et al., 1989) this questionnaire presented a
Cronbach’s Alpha of 0.85 (M=2.49; SD=0.65).
In turn, financial threat was assessed by the Financial Threat Scale (FTS) (Marjanovic et al.,
2013) composed by five items organized in a five-point Likert-scale (1 – Not at all; 5 – Extremely
Uncertain). In the validation study of FTS (Marjanovic et al., 2013) a Cronbach’s Alpha value of
0.91 was obtained (M=3.30; SD=0.86).
Financial well-being was evaluated with the Financial Well-Being Scale (FWBS) (Norvilitis et
al., 2003) that included eight items with a five-point Likert-Scale (1 – Strongly Disagree; 5–
Strongly Agree). The Cronbach’s Alpha achieved in the validation process of FWBS (Norvilitis et
al., 2003) was 0.74 (M=24.77; SD=5.92).
Lastly, stress, anxiety, and depression were measured by the Depression Anxiety Stress Scale
21-item version (DASS-21) (Lovibond & Lovibond, 1995), using the adapted version for the
Portuguese population of Pais-Ribeiro, Honrado, and Leal (2004). This scale was composed by 21
items, seven items for each construct (i.e, stress, anxiety, and depression), and possessed a four-
point answer scale (0 - Did not aply to me at all - Never; 3 - Applied to me very much, or most of
the time - Almost Always). The Cronbach's Alpha of this scale for the constructs stress, anxiety, and
depression was, respectively: (a) 0.92 (M=7.17; SD= 5.39); (b) 0.90 (M=4.06; SD=4.51); and (c)
0.86 (M=4.85; SD=4.84) (Lovibond & Lovibond, 1995). In the adaptation study for Portugal, the
Cronbach’s Alpha values achieved were higher than 0.70 (Pais-Ribeiro et al., 2004).
Procedures
The questionnaire, developed by Greenglass (2008), was applied between March-June 2013,
through an online survey which was distributed via an email that explained the objectives of the
research and provided information about the response link. An inclusion criterion set for the
questionnaires’ answer was that the respondents had to be over eighteen years old. As a result,
responses from participants younger than the abovementioned age were excluded from the sample.
The contacted participants were integrated in a contact database created from previous researches at
a Portuguese University. Furthermore, in the email sent to the participants, they were asked to
forward that message to their personal contacts. In the instructions section of the questionnaire,
which preceded the presentation of the instruments, a paragraph was included informing potential
participants that all the obtained responses were confidential and the information provided by the
respondents would only serve the purpose of this study.
Data Analysis Methods
In a first moment, an exploratory reliability analysis was conducted on the study’s variables and
items that compose their measurement. Some items’ scale on one measure were reverted, in order
that high values in all items express the same positive or negative perception on that measure. To
assess the measures’ reliability, the commonly indicators of scale reliability, Cronbach’s Alpha and
the Cronbach’s Alpha if Item Deleted, were observed. The Corrected Item-Total Correlation (CI-
TC) was computed, in order to inform about the relationships between individual items and each
scale. Items with a CI-TC lower than 0.3 can be excluded from the scale, since they reveal a weak
correlation with it (Betz, 2000). The items that were not eliminated as a result from the reliability
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analysis were considered as indicators in the structural equation model (SEM) used to test the
research hypotheses (Table 2).
SEM requires that data follows a multivariate normal distribution. There is no consensus about
the departures from normality that compromise the conclusions about the quality of the model
(Finney & DiSefano, 2006). However, these authors and others (e.g., Curran, West, & Finch, 1996)
refer that the most commonly used estimation methods in SEM, the maximum likelihood (ML) and
the generalized least squares (GLS) estimation methods, still produce proper results if items report
skewness and kurtosis values lower than 2 and 7, respectively. Regarding our study, all items
presented in table 2 fulfilled this condition. SEM was then used to examine the relationship between
the economic stress variables and the psychological health variables. AMOS 20 was used to
estimate and evaluate the model and the ML estimation method was applied. The analysis of the
overall model fit relied on three types of measures: (a) absolute fit; (b) incremental fit; and (c)
parsimonious fit (Hair, Anderson, Tatham, & Black, 1998). The measurement model was assessed
in terms of reliability and (convergent and discriminant) validity. The research hypotheses were
tested by observing the sign and statistical significance of the estimated path coefficients.
RESULTS
Overall model fit
Overall model fit indicates the extent to which the chosen indicators represent the hypothesized
constructs. Regarding the absolute fit evaluation, the Chi-square statistics is high and statistically
significant (χ2=2001.65; p = 0.000), failing to support a non-significant difference between the
actual and the predicted models. However, since this statistics is quite sensitive to sample size and
model complexity, other indices assessing the discrepancy between the actual and the predicted
models should be observed (Anderson & Gerbing, 1982). The results for these indices are
summarized in table 2. The Goodness of Fit Index (GFI) and the Root Mean Square Error of
Approximation (RMSEA) are 0.86 and 0.06, respectively. The registered fit value for the RMSEA
can be considered as satisfactory, however the value of the GFI (0.86) is lower than the cut-off of
0.90, which indicates a good fit according to Marôco (2014). Nonetheless, due to the proximity of
these values (i.e., 0.86 and 0.90) it was decided to accept the GFI score, considering it as suggesting
an acceptable fit. Incremental fit measures, which compare the model’s fit compared to a null model
(i.e., a model with no relations among the constructs and the indicators), ranged from 0.84 to 0.92,
suggesting a moderate to good incremental adjustment. The same finding applies to the
parsimonious fit indices that assess the model’s goodness of fit, but considering the number of
parameters being estimated.
Table 2.
Overall model fit indices
Goodness of fit criterion Observed value Comment
Absolute fit measures
GFI 0.86 Acceptable fit
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RMSEA 0.06 Satisfactory fit
Incremental fit measures
AGFI 0.84 Satisfactory fit
CFI 0.92 Good fit
NFI 0.89 Satisfactory fit
TLI 0.91 Good fit
IFI 0.92 Good fit
Parsimonious for measures
PCFI 0.84 Good fit
PNFI 0.81 Good fit
χ2/df 3.46 Satisfactory fit
Note. GFI: Goodness of Fit Index; RMSEA: Root Mean Square Error of
Approximation; AGFI: Adjusted Goodness of Fit Index; CFI: Comparative Fit Index;
NFI: Normed Fit Index; TLI: Tucker-Lewis Index; IFI: Incremental Fit Index; PCFI:
Parsimony Comparative Fit Index; PNFI: Parsimony Normed Fit Index; χ2: Chi-square
test; df: Degrees of freedom.
Measurement model fit
Before assessing the structural model (and thus make conclusions about the research
hypotheses), an adequate measurement model is necessary (Anderson & Gerbing, 1982). So, with
an acceptable overall model fit, our evaluation proceeds with the analysis of the measurement
model in terms of reliability and validity (Table 3). Starting with individual reliability, we can see
that all standardized factor loadings are higher than the threshold value of 0.5 and are significant at
the 0.01 level (p = 0.000). These results show that all indicators are positively and significantly
related to their specified constructs. Construct reliability is also very good since the Cronbach’s
Alpha and the composite reliability (CR) coefficients exceed 0.8 (Kline, 1998).
Table 3.
Measurement model fit indices
Latent variables and indicators Std. loadings* CR AVE
Financial Threat 0.89 0.62
FT1 - How uncertain do you feel? 0.83
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FT2 - How much do you feel at risk? 0.92
FT3 - How much do you feel threatened? 0.91
FT4 - How much do you worry about it? 0.70
FT5 - How much do you think about it? 0.65
Economic Hardship 0.87 0.53
EH1 - Cut back on social activities and entertainment expenses. 0.74
EH2 - Postpone major household purchases. 0.72
EH3 - Postpone clothing purchases. 0.74
EH4 - Change transportation patterns to save money. 0.59
EH5 - Change food shopping or eating habits to save money. 0.71
EH7 - Reduce household utility use. 0.77
Financial Well-Being 0.82 0.53
FWB1 - I am uncomfortable with the amount of debt I am in. 0.89
FWB2 - I worry about repaying my loans. 0.86
FWB3 -I worry about repaying my credit cards. 0.68
FWB5 - I think a lot about the debt I am in. 0.84
Stress 0.93 0.64
S1 - I found it hard to wind down. 0.75
S2 - I tended to over-react to situations. 0.74
S3 - I felt that I was using a lot of nervous energy. 0.76
S4 - I found myself getting agitated. 0.81
S5 - I found it difficult to relax. 0.83
S6 - I was intolerant of anything that kept me from getting on with what I
was doing.
0.74
S7 - I felt that I was rather touchy. 0.83
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Anxiety 0.91 0.60
A1 - I was aware of dryness of my mouth. 0.60
A2 - I experienced breathing difficulty. 0.66
A3 - I experienced trembling. 0.70
A4 - I was worried about situations in which I might panic and make a
fool of myself.
0.76
A5 - I felt I was close to panic. 0.82
A6 - I was aware of the action of my heart in the absence of physical
exertion.
0.68
A7 - I felt scared without any good reason. 0.79
Depression 0.92 0.63
D1 - I couldn't seem to experience any positive feeling at all. 0.73
D2 - I found it difficult to work up the initiative to do things. 0.67
D3 - I felt that I had nothing to look forward to. 0.77
D4 - I felt down-hearted and blue. 0.82
D5 - I was unable to become enthusiastic about anything. 0.82
D6 - I felt I wasn't worth much as a person. 0.76
D7 - I felt that life was meaningless. 0.66
Note. Std. loadings: Standardized factor loadings; CR: Composite reliability; AVE:
Average Variance Extracted; *For all standardized loadings: p = 0.000.
The average variance extracted (AVE) is a commonly used measure of convergent validity
showing the degree to which items’ behaviour is explained by the construct. One construct is
considered to have sufficient convergent validity when its AVE is higher than 0.5 (Fornell &
Lacker, 1981). In our study, all six constructs surpass this threshold value. Lastly, discriminant
validity evaluates the extent to which the indicators of one construct are not too related with other
constructs. A commonly used way to detect discriminant validity is to verify if the AVE value for
one construct is higher than any squared correlation between this construct and the remaining. Table
4 shows that discriminant validity is upheld for our measurement model regarding both the
economic stress variables (variables 1 to 3) and the psychological health variables (variables 4 to 6).
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Financial
Threat
Economic
Hardship
Anxiety (R2 = 0.15)
Financial
Wellbeing
Depression
(R2 = 0.21)
Stress (R2 = 0.22)
0.24
0.14
0.28
0.26
0.18
0.06 0.16
0.11
Table 4.
Discriminant validity assessment*
Latent variables 1. 2. 3. 4. 5. 6.
1. Financial Threat 0.69
2. Economic Hardship 0.28 0.53
3. Financial Well Being 0.14 0.13 0.53
4. Stress --- --- --- 0.64
5. Anxiety --- --- --- 0.64 0.60
6. Depression --- --- --- 0.64 0.54 0.63
Note.*Bolded values are the AVE’s. The remaining values are the squared correlations.
Structural model parameters
Figure 2 shows the standardized path estimates on the model itself. Results show that all
estimated coefficients have the expected positive signal. With the exception of the path relationship
between stress and financial well-being, all paths proposed in the conceptual model are statistically
significant (p < 0.01). So, excluding hypothesis 7, all the research hypotheses are not rejected,
supporting causal relationships among the economic stress variables and the psychological health
variables. The squared multiple correlations (SMC) for the endogenous constructs (i.e., stress,
anxiety, and depression) are within the ellipses. The low values for the SMCs are not surprising,
because it is expected that other constructs, not considered in this study, are also important
predictors of the psychological health variables. Since the regression weights are standardized, we
can conclude that the stronger relationships are those involving economic hardship and stress (path
estimate = 0.26), on one hand, and financial threat and stress (path estimate = 0.24), on the other
hand. The weaker relationships were found between financial well-being and depression (path
estimate = 0.11), and economic hardship and depression (path estimate = 0.18).
Figure 2.
Structural model results. The solid lines indicate paths statistically significant (p < 0.01), while
the dashed line indicates a non-significant path (p > 0.05).
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DISCUSSION
In sum, our study demonstrated that during phases of economic turmoil aspects like financial
threat and economic hardship (i.e., economic stressors) have a significant impact on stress, anxiety,
and depression (i.e., mental health indicators). The obtained results are congruent with the existing
literature. The reviews of Catalano (1991) and Catalano et al. (2011) verified that symptoms of
stress, anxiety, and depression increase in periods of economic recession. Past researches performed
by Butterworth et al. (2009), Greenglass et al. (2013), and Sargent-Cox et al. (2011) demonstrated
that anxiety and depression are highly correlated with adverse financial cycles. Lastly, Leal et al.
(2014) also observed the existence of a significant impact of economic stressors on mental health
indicators. However, although financial well-being has been significantly associated with anxiety
and depression, it was not a significant predictor of stress. This is partially confirmed by other
studies (e.g., Weisman, 2002; Norvilitis et al. 2003), which have demonstrated that perceived
financial well-being is related to one’s overall psychological well-being.
In other words, our data confirms that, overall, the economic recession influences individual’s
psychological health, since economic stressors (i.e., economic hardship and financial threat) are
significant predictors of mental health indicators (i.e., stress, anxiety, and depression). Therefore,
the present research contributes to the current state of the art about the psychological impact of the
economic recession, since eight of the nine hypotheses created were confirmed, and the only
hypothesis that was not confirmed, given that the statistical results were not significant, pointed in
the same direction, the existence of an influence of economic stressors on subject’s mental health.
Future researches performed about the relationship established between economic stressors and
mental health indicators should use other methodological designs (e.g., longitudinal designs),
because that will allow comparisons between the aforementioned aspects in distinct economic
periods (e.g., economic recession vs. economic growth). Furthermore, the sampling criteria ought to
be more accurate, including subjects that belong to different social and economic backgrounds.
Another interesting proposal is try to understand the reason why financial well-being does not have
a significant impact on stress. Ultimately, it would be useful to realize similar studies in other
European countries affected by financial crises (e.g., Cyprus, Greece, Ireland, and Spain) and
compare the obtained results.
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